Object recognition using summed features classifier

Authors:
Marcus Lindner;Marco Block;Raúl Rojas
Affiliations:
Institut für Informatik und Mathematik, Free University of Berlin, Berlin, Germany;Institut für Informatik und Mathematik, Free University of Berlin, Berlin, Germany;Institut für Informatik und Mathematik, Free University of Berlin, Berlin, Germany
Venue:
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Year:
2012

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Abstract

A common task in the field of document digitization for information retrieval is separating text and non-text elements. In this paper an innovative approach of recognizing patterns is presented. Statistical and structural features in arbitrary number are combined into a rating tree, which is an adapted decision tree. Such a tree is trained for character patterns to distinguish text elements from non-text elements. First experiments in a binarization application have shown promising results in significant reduction of false-positives without producing false-negatives.